APPROACHES FOR LOSSY COMPRESSION USING MACHINE LEARNING

Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Information Disclosure Statement The information disclosure statement (IDS) submitted on 01/10/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Double Patenting The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969). A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13. The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer. Claims 1, 2, 4-12, and 14-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2, 4, 5, and 16-20 of U.S. Patent No. 12288365. Although the claims at issue are not identical, they are not patentably distinct from each other because the language of the claims in the current application are obvious in view of the language of the claims in the patent. Below table shows how the claim language of the current application correspond to the language of the comparable claims in the patent: App. No. 19/016,670 Us pat. no. 12, 288,365 1. A computing system comprising: one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the system to perform: obtaining a media, the media including an image, audio, video, or combination thereof; receiving an input regarding one or more features across one or more frames of the media to be maintained in or removed from the media, wherein the receiving of the input comprises receiving a selection, from the one or more features, of a first feature while a second feature remains unselected; inferring a first criteria and a second criteria of a lossy compression technique, based on the received input, wherein the inferred first criteria is applied to any first features, from the one or more features, satisfying a degree of similarity of a classification or characteristic compared to the first feature and the inferred second criteria is applied to one or more second features, from the one or more features, failing to satisfy the degree of similarity of the classification or characteristic compared to the first feature; and applying the inferred first criteria and the inferred second criteria of the lossy compression technique to the media, wherein the applying of the inferred first criteria and the inferred second criteria comprises applying the inferred first criteria to the any first features and applying the inferred second criteria to the one or more second features. 1. A computing system comprising: one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the system to perform: obtaining a media, the media including an image, audio, video, or combination thereof; receiving an input regarding one or more features and frames of the media to be maintained in or removed from the media, wherein the receiving of the input comprises receiving a selection of a first feature while a second feature remains unselected; inferring, using a machine learning model, a first criteria and a second criteria of a lossy compression technique, based on the received input, wherein the inferred first criteria is applied to any first features matching or having a same classification as the first feature and the inferred second criteria is applied to one or more second features having a different classification compared to the first feature, wherein the inferred first criteria or the inferred second criteria is based on one or more relative amounts or rates of movement of one or more objects across the one or more frames; and applying the inferred first criteria and the inferred second criteria of the lossy compression technique to the media, wherein the applying of the inferred first criteria and the inferred second criteria comprises applying the inferred first criteria to the any first features and applying the inferred second criteria to the one or more second features. 2. The computing system of claim 1, wherein the instructions further cause the system to perform: in response to applying the inferred first criteria and the inferred second criteria of the lossy compression technique, receiving an additional input; adjusting the inferred first criteria or the inferred second criteria based on the additional input; and reapplying the adjusted inferred first criteria or the adjusted inferred second criteria of the lossy compression technique to the media. 2. The computing system of claim 1, wherein the instructions further cause the system to perform: in response to applying the inferred first criteria and the inferred second criteria of the lossy compression technique, receiving an additional input; adjusting the inferred first criteria or the inferred second criteria based on the additional input; and reapplying the adjusted inferred first criteria or the adjusted inferred second criteria of the lossy compression technique to the media. 4. The computing system of claim 1, wherein the inferred first criteria or the inferred second criteria is determined based on a scale of a byte or a frame, the criteria indicating whether to downsample, compress, keep, or remove the byte or the frame. 4. The computing system of claim 1, wherein the inferred first criteria or the inferred second criteria is determined based on a scale of a byte or a frame, the criteria indicating whether to downsample, compress, keep, or remove the byte or the frame. 5. The computing system of claim 4, wherein the inferred first criteria or the inferred second criteria further comprises, in response to determining to remove the byte or the frame, determine whether to purge the byte or the frame or to store the byte or the frame in a tiered storage. 5. The computing system of claim 4, wherein the inferred first criteria or the inferred second criteria further comprises, in response to determining to remove the byte or the frame, determine whether to purge the byte or the frame or to store the byte or the frame in a tiered storage. 6. The computing system of claim 1, wherein the instructions that, when performed by the one or more processors, further cause the computing system to perform: generating a compressed version of the media by applying the lossy compression technique, the lossy compression technique resulting in compressed features; and storing the compressed version and the compressed features of the media within a storage according to a storage criteria, wherein the storage criteria comprises storing portions of the compressed version or the compressed features that have higher information content or higher information value within a first location and portions of the compressed version or the compressed features having lower information content or lower information value within a second location, wherein the first location is configured to retrieve the compressed version of the unstructured information faster compared to the retrieving of the portions of the compressed features from the second location. 16. The computing system of claim 1, wherein the instructions that, when performed by the one or more processors, further cause the computing system to perform: generating a compressed version of the media by applying the lossy compression technique, the lossy compression technique resulting in compressed features; storing the compressed version and the compressed features of the media within a storage according to a storage criteria, wherein the storage criteria comprises storing portions of the compressed version or the compressed features that have higher information content or higher information value within a first location and portions of the compressed version or the compressed features having lower information content or lower information value within a second location, wherein the first location is configured to retrieve the compressed version of the unstructured information faster compared to the retrieving of the portions of the compressed features from the second location. 7. The computing system of claim 1, wherein the inferred first criteria compresses the any first features at a first compression rate and the inferred second criteria compresses the one or more second features at a second compression rate. 17. The computing system of claim 1, wherein the inferred first criteria compresses the any first features at a first compression rate and the inferred second criteria compresses the one or more second features at a second compression rate. 8. The computing system of claim 7, wherein the first compression rate is higher than the second compression rate. 18. The computing system of claim 17, wherein the first compression rate is higher than the second compression rate. 9. The computing system of claim 7, wherein the inferred first criteria and the inferred second criteria are applied to the any first features and the one or more second features within a same image frame. 19. The computing system of claim 1, wherein the inferred first criteria and the inferred second criteria are applied to the any first features and the one or more second features within a same image frame. 10. The computing system of claim 1, wherein the inferred first criteria or the inferred second criteria is based on relative amounts or rates of movement of the one or more features. 1. A computing system comprising: one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the system to perform: obtaining a media, the media including an image, audio, video, or combination thereof; receiving an input regarding one or more features and frames of the media to be maintained in or removed from the media, wherein the receiving of the input comprises receiving a selection of a first feature while a second feature remains unselected; inferring, using a machine learning model, a first criteria and a second criteria of a lossy compression technique, based on the received input, wherein the inferred first criteria is applied to any first features matching or having a same classification as the first feature and the inferred second criteria is applied to one or more second features having a different classification compared to the first feature, wherein the inferred first criteria or the inferred second criteria is based on one or more relative amounts or rates of movement of one or more objects across the one or more frames; and applying the inferred first criteria and the inferred second criteria of the lossy compression technique to the media, wherein the applying of the inferred first criteria and the inferred second criteria comprises applying the inferred first criteria to the any first features and applying the inferred second criteria to the one or more second features. 11. A computer-implemented method, wherein the method is performed using one or more processors, the method comprising: obtaining a media, the media including an image, audio, video, or combination thereof; receiving an input regarding one or more features across one or more frames of the media to be maintained in or removed from the media, wherein the receiving of the input comprises receiving a selection, from the one or more features, of a first feature while a second feature remains unselected; inferring a first criteria and a second criteria of a lossy compression technique, based on the received input, wherein the inferred first criteria is applied to any first features, from the one or more features, satisfying a degree of similarity of a classification or characteristic compared to the first feature and the inferred second criteria is applied to one or more second features, from the one or more features, failing to satisfy the degree of similarity of the classification or characteristic compared to the first feature; and applying the inferred first criteria and the inferred second criteria of the lossy compression technique to the media, wherein the applying of the inferred first criteria and the inferred second criteria comprises applying the inferred first criteria to the any first features and applying the inferred second criteria to the one or more second features. 20. A computer-implemented method, wherein the method is performed using one or more processors, the method comprising: obtaining a media, the media including an image, audio, video, or combination thereof; receiving an input regarding one or more features and frames of the media to be maintained in or removed from the media, wherein the receiving of the input comprises receiving a selection of a first feature while a second feature remains unselected; inferring, using a machine learning model, a first criteria and a second criteria of a lossy compression technique, based on the received input, wherein the inferred first criteria is applied to any first features matching or having a same classification as the first feature and the inferred second criteria is applied to one or more second features having a different classification compared to the first feature, wherein the inferred first criteria or the inferred second criteria is based on one or more relative amounts or rates of movement of one or more objects across the one or more frames; and applying the inferred first criteria and the inferred second criteria of the lossy compression technique to the media, wherein the applying of the inferred first criteria and the inferred second criteria comprises applying the inferred first criteria to the any first features and applying the inferred second criteria to the one or more second features. 12. The computer-implemented method of claim 11, further comprising: in response to applying the inferred first criteria and the inferred second criteria of the lossy compression technique, receiving an additional input; adjusting the inferred first criteria or the inferred second criteria based on the additional input; and reapplying the adjusted inferred first criteria or the adjusted inferred second criteria of the lossy compression technique to the media. 2. The computing system of claim 1, wherein the instructions further cause the system to perform: in response to applying the inferred first criteria and the inferred second criteria of the lossy compression technique, receiving an additional input; adjusting the inferred first criteria or the inferred second criteria based on the additional input; and reapplying the adjusted inferred first criteria or the adjusted inferred second criteria of the lossy compression technique to the media. 14. The computer-implemented method of claim 11, wherein the inferred first criteria or the inferred second criteria is determined based on a scale of a byte or a frame, the criteria indicating whether to downsample, compress, keep, or remove the byte or the frame. 4. The computing system of claim 1, wherein the inferred first criteria or the inferred second criteria is determined based on a scale of a byte or a frame, the criteria indicating whether to downsample, compress, keep, or remove the byte or the frame. 15. The computer-implemented method of claim 14, wherein the inferred first criteria or the inferred second criteria further comprises, in response to determining to remove the byte or the frame, determine whether to purge the byte or the frame or to store the byte or the frame in a tiered storage. 5. The computing system of claim 4, wherein the inferred first criteria or the inferred second criteria further comprises, in response to determining to remove the byte or the frame, determine whether to purge the byte or the frame or to store the byte or the frame in a tiered storage. 16. The computer-implemented method of claim 11, further comprising: generating a compressed version of the media by applying the lossy compression technique, the lossy compression technique resulting in compressed features; and storing the compressed version and the compressed features of the media within a storage according to a storage criteria, wherein the storage criteria comprises storing portions of the compressed version or the compressed features that have higher information content or higher information value within a first location and portions of the compressed version or the compressed features having lower information content or lower information value within a second location, wherein the first location is configured to retrieve the compressed version of the unstructured information faster compared to the retrieving of the portions of the compressed features from the second location. 16. The computing system of claim 1, wherein the instructions that, when performed by the one or more processors, further cause the computing system to perform: generating a compressed version of the media by applying the lossy compression technique, the lossy compression technique resulting in compressed features; storing the compressed version and the compressed features of the media within a storage according to a storage criteria, wherein the storage criteria comprises storing portions of the compressed version or the compressed features that have higher information content or higher information value within a first location and portions of the compressed version or the compressed features having lower information content or lower information value within a second location, wherein the first location is configured to retrieve the compressed version of the unstructured information faster compared to the retrieving of the portions of the compressed features from the second location. 17. The computer-implemented method of claim 11, wherein the inferred first criteria compresses the any first features at a first compression rate and the inferred second criteria compresses the one or more second features at a second compression rate. 17. The computing system of claim 1, wherein the inferred first criteria compresses the any first features at a first compression rate and the inferred second criteria compresses the one or more second features at a second compression rate. 18. The computer-implemented method of claim 17, wherein the first compression rate is higher than the second compression rate. 18. The computing system of claim 17, wherein the first compression rate is higher than the second compression rate. 19. The computer-implemented method of claim 17, wherein the inferred first criteria and the inferred second criteria are applied to the any first features and the one or more second features within a same image frame. 19. The computing system of claim 1, wherein the inferred first criteria and the inferred second criteria are applied to the any first features and the one or more second features within a same image frame. 20. The computer-implemented method of claim 11, wherein the inferred first criteria or the inferred second criteria is based on relative amounts or rates of movement of the one or more features. 1. A computing system comprising: one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the system to perform: obtaining a media, the media including an image, audio, video, or combination thereof; receiving an input regarding one or more features and frames of the media to be maintained in or removed from the media, wherein the receiving of the input comprises receiving a selection of a first feature while a second feature remains unselected; inferring, using a machine learning model, a first criteria and a second criteria of a lossy compression technique, based on the received input, wherein the inferred first criteria is applied to any first features matching or having a same classification as the first feature and the inferred second criteria is applied to one or more second features having a different classification compared to the first feature, wherein the inferred first criteria or the inferred second criteria is based on one or more relative amounts or rates of movement of one or more objects across the one or more frames; and applying the inferred first criteria and the inferred second criteria of the lossy compression technique to the media, wherein the applying of the inferred first criteria and the inferred second criteria comprises applying the inferred first criteria to the any first features and applying the inferred second criteria to the one or more second features. Claims 3 and 13 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2, and 20 of U.S. Patent No. 12288365 in view of us patent application no. 2018/0063538 to Bernal et al. (hereinafter Bernal). While Claims 1, 2 and 20 of the patent teaches performing the recited steps of claims 3 and 13, it does not explicitly teach that the steps are performed for the additional input. Bernal in the analogous art teaches dealing with the additional data (see, e.g., par. 18 and FIG. 1 of Bernal, which teach adjusting the compression parameters based on the additional data). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to return and repeat the processing for the additional input as taught by Beckmann because doing so would yield predicable results of more effectively dealing with the additional data by dynamically adjusting the compression parameters (see MPEP 2143(I)(D)). Additional Citations The following table lists several references that are relevant to the subject matter claimed and disclosed in this Application. The references are not relied on by the Examiner, but are provided to assist the Applicant in responding to this Office action. Citation Relevance Priebe et al. (us pat. pub. 2021/0112256) Describes semantically-segmented video compression. On embodiment includes loading a frame of video imagery, determining a context for the frame, selecting an object of interest for the context and identifying within the frame, a portion of the frame of the imagery deemed to be of greater interest than other portions of the frame of video imagery based upon the object of interest of the context determined for the video imagery, and an object, that has been pre-specified to be of importance to the context. A hybrid compression of the video imagery is then performed that includes both a higher quality compression of the portion of the video imagery determined to be a region of higher interest that produces a minimization of loss during decompression and also a compression of the other portions of the video imagery determined not to be a region of higher interest that produces more loss during decompression. Wei et al. (us pat. 10419773) Describes techniques that employ both supervised and unsupervised machine learning to classify video content for compression using encoding profiles that are optimized for each type of video content. Video samples are initially classified into one of a relatively small number of semantic classes by a classifier that is trained using known training samples. Video samples assigned to a particular semantic class are then further classified into one of a number of sub-groups within that class that are derived using an unsupervised clustering technique on the training samples assigned to that semantic class. Each cluster represents video samples having similar encoding complexity. A video sample assigned to a particular cluster is then encoded using an encoding profile optimized for that cluster. An example will be instructive. Table 1 Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See Table 1 and form 892. Any inquiry concerning this communication or earlier communications from the examiner should be directed to WOO RHIM whose telephone number is (571)272-6560. The examiner can normally be reached Mon - Fri 9:30 am - 6:00 pm et. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /WOO C RHIM/ Examiner, Art Unit 2676